Samuel Kamande is a Data Scientist at Nielsen and his presentation will focus on “Paradigm Shift in Research”. We caught up with him and he shared a lot about his work at Nielsen, some of the projects he has worked on like “Digital Divide project in Trinidad and Tobago in 2013”,thoughts on the future of Data Science and something...
Curse of Dimensionality:One of the most commonly faced problems while dealing with data analytics problem such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios,...
I sat down with former rugby school captain whose rugby career was cut short by a shoulder injury while playing for Black Blad at Kenyatta University. It is always a great pleasure to talk to someone who is extremely passionate about what he does and his passion for Data Science was evident during my chat with “BlackOrwa” at iHub...
In January 2016, I was honored to receive an “Honorable Mention” of the
John Chambers Award 2016.
This article was written for R-bloggers,
whose builder, Tal Galili, kindly invited me
to write an introduction to the rARPACK package.
A Short Story of rARPACK
Eigenvalue decomposition is a commonly used technique in
numerous statistical problems. For example, principal component analysis (PCA)
basically conducts eigenvalue...
In order to illustrate hierarchical clustering techniques and k-means, I did borrow François Husson‘s dataset, with monthly average temperature in several French cities. > temp=read.table( + "http://freakonometrics.free.fr/FR_temp.txt", + header=TRUE,dec=",") We have 15 cities, with monthly observations > X=temp > boxplot(X) Since the variance seems to be rather stable, we will not ‘normalize’ the variables here, > apply(X,2,sd) Janv Fevr Mars...
Another popular application of classification techniques is on texmining (see e.g. an old post on French president speaches). Consider the following example, inspired by Nobert Ryciak’s post, with 12 wikipedia pages, on various topics, > library(tm) > library(stringi) > library(proxy) > titles = c("Boosting_(machine_learning)", + "Random_forest", + "K-nearest_neighbors_algorithm", + "Logistic_regression", + "Boston_Bruins", + "Los_Angeles_Lakers", + "Game_of_Thrones", + "House_of_Cards_(U.S._TV_series)", + "True Detective...
This document contains a startup script for H2O in R. It is a silly example (why would anybody want to train a deep encoder on the iris dataset) but it helps people get started. This setup is meant for local use, not for cluster setup.
Just copy the code in!
h2o.init(ip = 'localhost', port = 54321, nthreads= -1, max_mem_size...
Data analyses are the product of many different tasks, and statistical methods are one key aspect of any data analysis. There is a common workflow in the related areas of informatics, data mining, data science, machine learning, and statistics. The workflow tasks include data preparation, the development of predictive mathematical models, and the interpretation and Read More ...The...
(Post by Dirk Eddelbuettel and JJ Allaire) A common theme over the last few decades was that we could afford to simply sit back and let computer (hardware) engineers take care of increases in computing speed thanks to Moore’s law. That same line of thought now frequently points out that we are getting closer and closer
Simple image recognition app using TensorFlow and Shiny
My weekend was full of deep learning and AI programming so as a milestone I made a simple image recognition app that:
Takes an image input uploaded to Shiny UI
Performs image recognition using TensorFlow
Plots detected objects and scores in wordcloud
This app is to demonstrate powerful image recognition...